Data and intelligence
Data and intelligence is the brain of the Gamble Hub, a system that senses, analyzes and acts. In classical models, data is the archive accessed after events. In Gamble Hub, they become a livestream, feeding solutions, models and automatic reactions.
Every event in the ecosystem - from click to transaction - turns into a signal. These signals are processed by machine models that recognize patterns, predict behavior, and help operators make decisions faster than manually possible.
The main idea: data is not collected for the sake of a report, it creates the semantic fabric of the system. Gamble Hub builds a chain:- telemetry → models → signals → operations.
1. Telemetry. The network captures millions of microevents: player activity, RTP changes, API delays, betting streams, user behavior.
2. Models. Machine learning algorithms identify anomalies, predict load peaks, determine stable patterns of profitability and risks.
3. Signals. Models generate signals - recommendations, warnings, automatic actions.
4. Operations. The system itself performs part of the decisions: adjusts the limits, informs operators, changes configurations and reports on opportunities.
This is how a self-learning infrastructure is created, where intelligence does not replace a person, but helps him see further and act faster.
The Gamble Hub data architecture is built around the principles of:- Transparency and verification. Each number has a fixation source and time.
- Contextuality. The model does not work with abstract values, but with reference to currencies, regions, providers and players.
- Continuing education. Algorithms are updated as new data becomes available, avoiding "outdated assumptions."
- Integration with operations. Models do not live in isolation - they are built into interfaces and APIs, turning analytics into action.
- Operational intelligence - instant reaction to events and deviations.
- Strategic intelligence - analysis of trends and formation of growth scenarios.
- Collective intelligence - synchronizing knowledge between circuits and participants.
Gamble Hub converts data from a byproduct into system energy.
Intelligence here is not a module or a service, but a built-in property of architecture that makes the ecosystem capable of introspection, adaptation and prediction of future states.
Data and intelligence are not just analytics. This is the awareness of the whole network.
In a world where speed is more important than size, the Gamble Hub makes intelligence the main tool for sustainable growth.
Key Topics
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Telemetry and Event Collection
A practical guide to telemetry design and event collection in the iGaming ecosystem: taxonomy and schematics, client and server instrumentation, OpenTelemetry, identifiers and correlation, data sampling and quality, PII privacy and minimization, transport and buffering, reliability and idempotency, observability and SLO, dashboards and implementation roadmap.
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Real-time signal processing
Practical architecture and patterns for real-time signal processing in iGaming: sources and taxonomy of events, CEP and stateful aggregations (window functions, watermarks, late data), enrichment and deduplication, antifraud and RG detectors, online features and scoring models, delivery guarantees and idempotency, scaling and cost, observability and SLO, dashboards, security and privacy, RACI and implementation roadmap with sample schemes and pseudo code.
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Data enrichment
A practical guide to data enrichment for the iGaming ecosystem: sources and types of enriching signals (FX/geo/ASN/devices, KYC/RG/AML, content and directories), offline and streaming pipelines (lookup, join, UDF/ML features), normalization currency and timezone, PII privacy and minimization, quality and DQ rules, observability and lineage, cost and SLO, architecture patterns (dimension lookup, feature store, async enrichment), SQL/YAML/pseudocode examples, RACI and implementation roadmap.
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Streaming and streaming analytics
Practical methodology for building streaming and streaming analytics for iGaming: ingest→shina→obrabotka→serving architecture, windows and watermarks, CEP and stateful aggregation, exactly-once/idempotency, schemes and contracting, real-time showcases and ClickHouse/Pinot/Druid, observability and SLO, privacy and regionalization, cost-engineering, RACI and roadmap, with SQL/pseudocode examples.
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Batch processing
A practical guide to batch data processing for the iGaming platform: ingest→lakehouse→orkestratsiya→vitriny architecture, incremental downloads and CDC, SCD I/II/III, backfill and reprocessing, quality control (DQ-as-code), data privacy and residency, cost and performance optimization, observability and SLO, schemes/contracts, examples SQL/YAML and implementation roadmap.
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Real-time analytics
Full guide to real-time analytics for the iGaming ecosystem: business cases (AML/RG, operational SLAs, product personalization), ingest→shina→stream reference architecture - obrabotka→real-time showcases, CEP and stateful aggregations, watermarks/late data, online enrichment and Feature Store, metrics and SLO, observability and cost engineering, privacy and residency, SQL/pseudocode templates, RACI, and implementation roadmap.
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Stream vs Batch analysis
Stream and Batch Analysis Comparison Guide for iGaming: Architectures (Lambda/Kappa/Lakehouse-Hybrid), Windows and Watermarks vs Increments and CDC, CEP/stateful-aggregations vs SCD and Snapshots, Latency/Completeness/Cost, DQ and reproducibility, privacy and residency, usage patterns (AML/RG/SRE/product/reporting), solution matrices, SQL/pseudocode examples, roadmap, RACI, and checklists.
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Machine learning in iGaming
Full ML application guide in iGaming: key cases (LTV/black, personalization, anti-fraud/AML, Responsible Gaming), data and features, online and offline scoring, Feature Store, MLOps (experiments, CI/CD/CT, monitoring and drift), offline/online metrics, A/B tests and causal approaches, privacy and compliance, surfing architecture (batch/real-time), cost engineering, RACI, roadmap and SQL/pseudocode examples.
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Teaching with and without a teacher
A comparative and practical guide to Supervised/Unsupervised approaches for iGaming: key cases (LTV/black, anti-fraud/AML, RG, personalization), task and metric selection, algorithms (classification/regression, clustering/anomalies/dimension reduction), semi/self-supervised, active learning, feature preparation and point-in-time, offline/online surfing and drift monitoring, privacy and compliance, cost engineering, RACI, roadmap, checklists and SQL/pseudocode examples.
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Reinforcement Training
RL Practice Guide (Renewal Learning) for iGaming: Cases (Personalization, Bonus Optimization, Game Recommendations, Operational Policies), Bandits/Contextual Bandits/Slate-RL, Offline/Batch-RL, Safe Limits (RG/AML/Compliance), Rewards, and Causal - evaluation, simulators and counterfactual-methods (IPS/DR), MLOps and serving (online/near-real-time), metrics and A/B, cost engineering, RACI, roadmap and checklists.
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Feature Engineering and Feature Selection
A practical guide to feature creation and selection for iGaming: point-in-time discipline, windows and aggregations (R/F/M), categorical encodings (TE/WOE), temporal/graph/NLP/geo-features, anti-leukage and online/offline reconciliation, Feature Store and tests equivalence, selection (filter/wrapper/embedded, SHAP/IV/MI), stability and drift, cost engineering (latency/cost per feature), RACI, roadmap, checklists and SQL/YAML/pseudocode examples.
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Model monitoring
ML model monitoring playbook in iGaming: SLI/SLO and operational metrics, data drift control/predictions (PSI/KL/KS), calibration (ECE), threshold stability and expected-cost, coverage and errors, slice/fairness analysis, online labels and delayed labels, alerts and runbook 'and, dashboards (Prometheus/Grafana/OTel), audit/PII/residency, RACI, roadmap and production readiness checklist.
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AI pipelines and training automation
Practical playbook on AI/ML pipeline design and automation in iGaming: orchestration (Airflow/Argo), data pipelines and feature (Feature Store), CT/CI/CD for models, registers and promotion policies, automatic retrain by drift, online/offline equivalence tests, security (PII/residency), RACI, roadmap, checklists and examples (DAG, YAML, pseudocode).
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Pattern recognition
A complete guide to pattern recognition: task types (classification, clustering, segmentation, sequences), data representations and features, classical and neural network methods (SVM, ensembles, CNN/RNN/Transformer, GNN), quality metrics, interpretability, robustness, and MLOps practices for implementation and monitoring in prode.
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KPIs and Benchmarks
System guide for KPIs and benchmarks: types of metrics (North Star, result/process, guardrail), formulas and norms, goal setting (SMART/OKR), normalization and seasonality, statistical stability, comparative bases (internal/external), dashboards, review cycles and anti-patterns (Goodhart).
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Data segmentation
A practical guide to data segmentation: segment goals and types (RFM, cohorts, behavioral, value, risk segments), methods (rules, clustering, factor/embeddings, supervise segmentation), quality and stability metrics, A/B validation, operational implementation, drift monitoring, and ethics.
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Data visualization
A practical guide to data visualization: goals and audiences, chart selection, composition and color, storytelling and annotations, dashboard design, readability metrics, accessibility, anti-patterns, and product and production tips.
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Metrics architecture
A practical guide to the metrics architecture: from definition and versioning to calculation (batch/stream), semantic layer and catalog, quality control, SLO freshness, security and trace auditor. Templates "passport metrics," "source contract," release and operation checklists.
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Key figure hierarchy
A practical guide to the hierarchy of indicators: how to choose North Star, decompose it into a driver tree, connect guardrail metrics, cascade goals by organization levels (OKR/KPI), agree on formulas in the semantic layer, set a freshness SLO and build a single cycle of review and development metrics.
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Correlation and Cause and Effect
A practical guide to correlation and causation: when correlation is sufficient, how to identify causality (A/B tests, DAG, back-door/front-door, IV, DiD, RDD, synthetic control), how to work with confounders, colliders and Simpson's paradox, and how to apply causal methods in the product marketing and ML.
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Path from signal to action
Signal-to-final scheme "Signal → Sense → Decide → Act → Learn": signal collection and normalization, dedup and prioritization, causality check, policy selection (rules/models/bandits), orchestration of actions, guardrails and hysteresis, effect measurement and feedback closure. Artifact templates, quality metrics, and checklists.
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KPI forecasting
Practical guide for KPI forecasting: task setting, data preparation, decomposition and regressors (holidays, promos), model selection (ARIMA/ETS/Prophet, GBM/NN, hierarchical and probabilistic), quality metrics and backtesting, scenario modeling, interval calibration, MLOps processes, monitoring and governorship.
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Risk modeling
Practical Guide to Risk Modeling: Threat Map and KRI, Frequency-Severity Models (Poisson/NegBin × Lognormal/Pareto), Compound Processes and LDA, EVT (GEV/GPD) and Thick Tails, Correlations and Copules, Stress Tests and Scenarios, Bayt es and Monte Carlo, VaR/CVaR, limits and RAROC, model governorship, drift monitoring and runibooks.
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Conversion Analytics
A practical guide to conversion analytics: how to correctly read funnels and coefficients, set "correct denominators" and time windows, exclude bots and duplicates, build cohorts and segments, associate conversion with LTV/CAC/ROMI, conduct experiments and avoid typical traps. Templates for metrics passports, pseudo-SQL and checklists.
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Recommendation systems
Practical guide to building recommendation systems: data and attribute space, architecture (candidate recall → ranking → policy-aware re-rank), models (content-based, collaborative filtering, factorization/embeddings, LTR/neural networks, session, contextual bandits and RL), goals and limitations (value, diversification, fairness, RG/compliance), offline/online metrics, A/B and causal assessment, MLOps/observability, anti-patterns and checklists.
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Player profiling
Practical Guide to Player Profiling: Goals and Applications (UX, Personalization, Risk/Compliance), Data Sources and Identities, Traits and Behavioral Patterns (RFM, Sessions, Content), Segmentation Techniques (Rules, Clusters, Embeddings, Propensities, Uplift), Profile Passports and Decision tables, Privacy/Ethics/RG, Monitoring and drift, MLOps-operation. Pseudo-SQL and artifact patterns.
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Behavioral cues
Practical guide to working with behavioral signals: what to collect (sessions, clicks, scrolling, dwell-time, trajectories), how to normalize and purify (idempotency, anti-bots, PIT), turn into signs (windows 5m/1h/24h, sequences, columns), measure quality (validity, attention, intention), protect privacy and safely use in products, analytics and ML.
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Origin and data path
A practical guide to building Data Lineage in the section "Data and Intelligence": levels (business, technical, column), end-to-end linage from sources to ML models, events and contracts, glossary and metadata, graph visualization, impact analysis, SLO/SLI freshness and quality, scripts for iGaming (KYC/AML, game rounds, payments, Responsible Gaming), artifact templates, and an implementation roadmap.
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Data ethics and transparency
A practical guide to data ethics in the Data and Intelligence section: principles (benefit, non-harm, fairness, autonomy, responsibility), transparency for players and regulators, honest personalization and marketing without manipulation, consent and minimization of data, work with vulnerable groups, explainability of ML (model cards, data statements), fairness metrics, policy templates and checklists for implementation.
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Data tokenization
Data & Intelligence Tokenization How to Guide: What Tokens Are and How They Differ from Encryption, Options (vault-based, vaultless/FPE), Detokenization Schemes, Rotation and Key Lifecycle, Integration with KYC/AML, Payments and Logs, Access Policy and Auditing, Performance and Resiliency, Metrics and Roadmap implementation. With artifact patterns, RACI and anti-patterns.
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Data security and encryption
Complete data protection guide in Data & Intelligence: threat model, transit and storage encryption (TLS/mTLS, AES-GCM, ChaCha20-Poly1305, TDE, FLE/AEAD), key management (KMS/HSM, rotation, split-key, envelope), secret management, signature and integrity (HMM AC/ECDSA), tokenization and masking, DLP and log sanitization, backup and DR, access and audit (RBAC/ABAC, JIT), compliance and privacy, SLO metrics, checklists, RACI and implementation roadmap. Focusing on iGaming cases: KYC/AML, payments, gaming events, Responsible Gaming.
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Data auditing and versioning
Audit and versioning practice guide in Data & Intelligence: audit logs (who/what/when/why), integrity and signature controls, change policy (SEMVER for schemas and storefronts), time-travel and snapshots, SCD/CDF, contract evolution of schemas, versioned feature store and ML models, procedures rollback/backfill, RACI, SLO metrics, checklists, and roadmap. Examples for iGaming: GGR edits, retro provider feed corrections, KYC/AML and RG reporting.
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DataOps-experts
DataOps Practice Guide in Data & Intelligence: Source to Dashboard/ML Value Flow, Contract-Oriented Development, CI/CD for Data, Testing (DQ/Schematics/Regression), Orchestration & Observability, Incident Management, Catalogs & Lineage, Environment Management, Releases (blue-green/canary), Security & Access, SLO metrics, artifact patterns, checklists, and road map. With examples for iGaming (KYC/AML, payments, gaming events, RG, marketing).
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NLP and word processing
Complete NLP Guide to Data and Intelligence: Text Collection and Normalization, Multilingualism and Slang, Purification and PII Revision, Tokenization/Lemmatization/Morphology, Vector Representations and Embeddings, Thematic Modeling and Classification, Entity/Relationship Extraction, Search (BM25 + Vector, RAG), Summarization, Q&A and chatbots, moderation/toxicity, OCR/ASR→tekst, quality metrics and MLOps, privacy/DSAR/ethics, pipeline templates and roadmap. With a focus on iGaming: support and chats, App Store/Google Play reviews, bonus rules, RG/AML risks, provider news and payment terms.
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Computer vision in iGaming
Computer Vision Application Practice Guide in Data & Intelligence: KYC/OCR and liveness, anti-fraud (bots/multi-account), banner/video moderation, UI/QA control, stream analytics (eSports/streamers), responsible advertising (RG), brand protection, A/Creative, synthetic data generation, quality metrics, privacy/biometrics/DSAR, architectures (on-device/edge/cloud, TEE), MLOps, SLO and roadmap. With a focus on multi-brand and multi-jurisdictional platforms.
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Multimodal models
Complete Guide to Multimodal Models in Data & Intelligence: Scripts for iGaming (KYC/liveness, creative moderation, stream analysis, RG/anti-fraud, support), Architecture (CLIP-like, Encoder-Decoder, Perceiver, LLM-as-orchestrator), data and markup (synchronization of modalities, synthetics, PII-edition), alignment (contrastive, ITC/ITM, instruction-tuning), privacy/biometrics/DSAR, metrics and benchmarks, MLOps (registry, canary, drift), cost/latency (quantization, cache, routing), API and SLO templates, checklists and roadmap.
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Data clustering
A practical guide to clustering in the section "Data and Intelligence": tasks and value without a teacher, preparation of signs (behavior, payments, games, devices), choice of algorithms (k-means/mini-batch, GMM, DBSCAN/HDBSCAN, spectral, hierarchical, SOM, mixed types), quality metrics (silhouette, Daae vies-Bouldin, stability), explainability and cluster profiles, online updates and drift, privacy (k-anonymity, tokenization), CRM/personalization/RG/anti-fraud integrations, pipeline templates, RACI, roadmap and anti-patterns.
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Dimensionality reduction
A Practical Guide to Dimensionality Reduction in Data and Intelligence: When and Why to Apply, Feature Sampling versus Factor Construction Difference, Methods (PCA/SVD, NMF/FA, t-SNE, UMAP, Autoencoders/Variac, PCA for Categorical Through Embeddings), Pipelines (Scaling, PII masks, time-travel), metrics (explained variance, trust/continuity, kNN-preservation), online updates and drift, cluster/anomaly visualization, privacy and k-anonymity, clustering/recommender/antifraud integrations, YAML patterns, and anti-patterns.
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Data schemas and their evolution
Complete Data & Intelligence Guide: Schema Design Principles (Tables, Events, Features), Notations (Avro/Protobuf/JSON Schema/DDL), Compatibility (backward/forward/full), Schema Contracts & Registers, Versions & Migrations (blue-green/dual-write/shadow-reads/backfill), the evolution of storefronts and Feature Store (SCD, semantic versions), directories/enum/locales, multi-brand/multi-jurisdictional and PII, compatibility tests and linters, anti-patterns, RACI and roadmap. Examples for iGaming: payments/PSP, game rounds, bonuses, RG/AML.
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Indexing Analytical Repositories
A practical guide to indexing in the Data & Intelligence section: index types (B-tree/Bitmap/Hash/BRIN/GiST/GIN/inverted/vector), partitioning and sorting (cluster keys, Z-order, order by), data skipping (min-max, bloom), materialized views, segment projections/clustering, results cache, statistics and optimizer, "small file" compaction, Iceberg/Delta/Hudi indexes on lakes, JSON/semi-structured fields, SCD patterns, monitoring and RACI. Examples of iGaming are payments/PSP, game rounds, RG/AML, and anti-fraud.
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Adaptive dashboards
A complete guide to designing and implementing adaptive dashboards: roles and context, personalization, device and channel response, availability, multi-tenancy, security, performance, experimentation, and success metrics.
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Big data insights
A practical guide to extracting business insights from Big Data: architecture and pipelines, analysis methods (descriptive/diagnostic/predictive/prescriptive analytics), experiments and causality, data i治理 quality, privacy and security, MLOps and operational support, success metrics and monetization.
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Decision cycles
A complete guide to designing, measuring, and optimizing decision cycles from question-and-answer and data mining to experimentation, automation, and operational reporting. Frameworks (OODA/PDCA/DIKW), roles and rights, speed/quality metrics, data and tool architecture, anti-patterns, roadmap and checklists.
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Thread prioritization
A practical guide to prioritizing data streams (batch/stream): business hierarchy and SLO, classes of service (QoS), multi-tenancy, schedulers and queues, backpressure and limits, cost-aware strategies, anti-patters, implementation roadmap, and production checklists.
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Compress analytical data
A practical guide to data compression for analytics: column formats (Parquet/ORC), codecs (ZSTD/Snappy/LZ4), encodings (RLE/Dictionary/Delta/Frame-of-Reference/Gorilla/XOR), time series and log compression, sketch - structures (HLL/TDigest), lossy/lossless compromises, impact on cost and SLO, encryption and compliance, compression and storage policies, testing and antipatterns.
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Audit of AI algorithms
ML/LLM Systems Audit Practice Guide: Objectives and Framework, Risk-Based Methodology, Documentation and Evidence, Data and Model Assessment (Quality, Equity, Privacy, Security, Sustainability), Red teaming, Online Monitoring and Incident Management, Compliance, Checklists, and Audit Implementation Roadmap as a Process.
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Adaptive model learning
A complete guide to adaptive learning (continuous/online/active/fine-tuning): drift types, retraining triggers, update strategies (batch/stream/partial/PEFT), personalization and multisegmentality, forgetting control, safe thresholds and guardrails, MLOps contour (versioning, rollbacks, monitoring), privacy and cost.
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Data integrity
A practical guide to ensuring data integrity throughout the circuit: integrity types (essential, reference, domain, business rules), contracts and schemes, transaction guarantees (ACID/isolation), distributed systems (idempotency, dedup, event order), DQ validation and tests, audit and lineage, security and privacy, roadmap and checklists.
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Real-time insights
A practical guide to organizing real-time insights: architecture (ingest→obrabotka→fichi→vitriny→dostavka), windows and watermarks, late/out-of-order, states and exactly-once in meaning, anomalies and causality, online experiments, SLO/observability, cost-aware strategies, security and privacy. With checklists, anti-patterns and policy templates.
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Data economics in iGaming
Practical guidance on economy of data in iGaming: the card of value and expenses (sborkhraneniyeobrabotkamodelideystviye), a unit economy (GGR, ARPPU, LTV, CAC, deduction), measurement of effect (uplift/increment), FinOps for data, prioritization of investments (real-time vs batch), compliance and privacy as a part of P&L, monetization of data (В2С/В2В/партнеры), the check sheets and templates the politician.
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AI visualization of metrics
AI visualization implementation guide: graph grammar and chart selection, NL→Viz (natural language in visual), auto-generation of dashboards, explanation of anomalies and causes, narratives and storytelling, RAG on metadata, quality and trust control, accessibility and privacy, SLO/cost, antipatterns, roadmap and checklists.